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Theoretical and Applied Genetics

, Volume 112, Issue 3, pp 462–471 | Cite as

Quantitative trait loci for grain moisture at harvest and field grain drying rate in maize (Zea mays, L.)

  • Rodrigo G. SalaEmail author
  • Fernando H. Andrade
  • Elsa L. Camadro
  • Julio C. Cerono
Original Paper

Abstract

Hybrids with low grain moisture (GM) at harvest are specially required in mid- to short-season environments. One of the most important factors determining this trait is field grain drying rate (FDR). To produce hybrids with low GM at harvest, inbred lines can be obtained through selection for either GM or FDR. Thus, a single-cross population (181 F 2:3-generation plants) of two divergent inbred lines was evaluated to locate QTL affecting GM at harvest and FDR as a starting point for marker assisted selection (MAS). Moisture measurements were made with a hand-held moisture meter. Detection of QTL was facilitated with interval mapping in one and two dimensions including an interaction term, and a genetic linkage map of 122 SSR loci covering 1,557.8 cM. The markers were arranged in ten linkage groups. QTL mapping was made for the mean trait performance of the F 2:3 population across years. Ten QTL and an interaction were associated with GM. These QTL accounted for 54.8 and 65.2% of the phenotypic and genotypic variation, respectively. Eight QTL and two interactions were associated with FDR accounting for 35.7 and 45.2% of the phenotypic and genotypic variation, respectively. Two regions were in common between traits. The interaction between QTL for GM at harvest had practical implications for MAS. We conclude that MAS per se will not be an efficient method for reducing GM at harvest and/or increasing FDR. A selection index including both molecular marker information and phenotypic values, each appropriately weighted, would be the best selection strategy.

Keywords

Linkage Group Inbred Line Marker Assisted Selection Physiological Maturity Grain Moisture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors thank Dr. Dario Bernacchi, Dr. Martín Grondona and Dr. Pablo Corva for their valuable suggestions during manuscript preparation. This work was supported by the Instituto Nacional de Tecnología Agropecuaria (INTA); Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); Facultad de Ciencias Agrarias Univ. Nac. de Mar del Plata (UNMdP) and Monsanto Argentina S.A. This work is a part of a thesis submitted by R.G. Sala in partial fulfillment for the requirements for the Doctor’s degree, UNMdP. R.G. Sala holds a scholarship from CONICET. The experiments performed comply with the current laws in Argentina.

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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Rodrigo G. Sala
    • 1
    • 2
    Email author
  • Fernando H. Andrade
    • 1
    • 2
  • Elsa L. Camadro
    • 1
    • 2
  • Julio C. Cerono
    • 3
    • 4
  1. 1.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Capital Federal, Buenos AiresArgentina
  2. 2.Unidad Integrada Balcarce, Instituto Nacional de Tecnología Agropecuaria (INTA)- Facultad de Cs. AgrariasUniversidad Nacional de Mar del Plata (UNMdP)Balcarce, Buenos AiresArgentina
  3. 3.Estación Experimental CametMonsanto Argentina S.A.Camet, Buenos AiresArgentina
  4. 4.KWS Argentina S.A.Balcarce, Buenos Aires Argentina

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